Machine Biography as Digital Human Twin: artistic explorations of predictive identity in the age of behavioural data

Abstract This essay addresses the emergence of Digital Human Twins through the prism of contemporary art, by presenting our recent project Machine Biography (2022), a speculative installation generated from a year-long experiment in self-surveillance and algorithmic inference. Each of the 365 autobiographical books created by the project use different open-sources artificial intelligence models to imagine a day in the life of the artist in the year 2050. The artwork, conceptualised as a fictional twin to a previous project, Data Biography (2017), explores how computational systems convert human identities into futures that can be predicted and influenced. Drawing on posthuman theory, critical data studies, and speculative aesthetics, we analyse Machine Biography as both an artistic simulation and a cultural critique of AI based forecasting. By transforming prediction into poetic fiction, the project challenges the utilitarian logic of digital twins and highlights the political, aesthetic and epistemic stakes of AI behavioural models. In doing so, it brings up urgent problems regarding autonomy, authorship and epistemic fairness in the era of algorithmic selfhood. Ultimately, Machine Biography does not represent identity—it performs it: as narrative, as pattern, as prediction. It illustrates the shift from data as record to data as speculation, confronting the determinist aspirations of AI with a counter-narrative imaginary in which futures are not forecasted but told, contested and reimagined.